It is well known in the art of geophysical prospecting to conduct MWD logging of earth formations while a well is being drilled and to conduct wireline logging of such earth formations after the well has been drilled, all in an attempt to evaluate the lithology of the formations, and thus to provide indications whether oil and/or gas can be produced out of such formations.
While the estimation of lithologic component fractions from well logs is common practice by those skilled in this art, existing methods do not correctly identify the range of possible solutions and some, assume the existence of a unique solution. There are in fact an infinite number of possible solutions that lie within a quantifiable range of values. In addition, some existing methods that attempt to determine a unique solution are based on a mathematical model involving the use of simultaneous equations. Key shortcomings of this approach include:
the assumption that each well log is equally accurate in distinguishing all lithologic components PA1 the assumption that all lithologic components must exist in pure form at a discrete value for a given log PA1 the assumption that each lithologic component exists to some extent over the entire range of possible log values (i.e. restricted ranges are not possible) PA1 the assumption that a linear relationship exists between log data and lithologic component fractions PA1 the true solution lies within a range defined by the two compositional limits and can be calibrated to a mineralogical analysis of actual core samples. This achieves the greatest possible verifiable accuracy from well logs. PA1 if sufficient core analyses are available to fully calibrate the lithology model, then it is theoretically possible to extract a more accurate porosity from any lithology-sensitive log suite including the neutron-density. This would then become the preferred method to determine porosity. PA1 a particular well log can be weighted to resolve a particular lithologic component more or less accurately than another log. For instance, the gamma ray may be 90% more accurate than the sonic log in resolving shale; on the other hand the sonic may be more accurate than the gamma ray in resolving non-shales. Prior art methods generally do not take this key factor into account. PA1 prior art mathematical models that rely on simultaneous equations to arrive at a solution are prone to computational problems including division by zero errors and negative component concentrations. The frequency of such errors increases exponentially as the number of lithologic components increases due to the nature of matrix algebra. Such errors also occur because of the incorrect assumption that all well logs are equally accurate in resolving all lithologic components, and because the model is not tolerant of errors in the well log data. Unfortunately, bad log data is an all too frequent occurrence in practice. It is probably fair to say that all well log data is flawed to some extent due to the complex interaction of a multitude of variables that exert an influence in the logging environment. For this reason it is crucial to calibrate any log derived analysis to a core analysis if possible. At present, core analysis is the only available means to absolutely verify a log derived analysis. PA1 the dual compositional model of the present invention is tolerant of bad log data and will consistently yield reasonable solutions where prior art methods would fail. Division by zero errors and negative concentration problems are avoided. PA1 because of the limitations of prior art methods, a satisfactory solution typically entails a time-consuming trial and error process, particularly with complex lithologies. The present invention minimizes this trial and error process. In short, it is significantly faster and more accurate than prior art methods. PA1 the method is applicable with any number or combination of lithology sensitive logs and any number or combination of lithologic components. This is a major advantage over prior art methods where the number of lithologic components is generally restricted by the number of well logs available. PA1 the simultaneous equations solution used by some prior art methods assumes that pure components exist at a discrete value for each log; in nature this is generally not the case. The method requires n logs to resolve n+1 components for a "unique" solution. If there are more than n+1 components, then a "maximum proportional mixture" solution is usually assumed where all components are present to the greatest extent possible; again, in nature this is generally not the case. PA1 ranges of existence for a each lithologic component can be specified based on laboratory measurement or experience with local geology. Prior art methods do not allow for such component ranges. PA1 the new method can model linear or non-linear behavior. Prior art methods generally assume linear behavior. PA1 with prior art methods it is very difficult, and often impossible, to calibrate a log-derived lithology analysis to a laboratory measurement of mineralogical composition from a core sample. With the dual compositional model this is always possible and readily achieved by design.
Empirical data shows these assumptions to be incorrect. The present invention addresses the problem by modeling lithology in two different ways to set compositional limits, thereby establishing the valid range of all possible solutions. A "pure component" model defines the upper compositional limit and a "proportional mixture" model defines the lower limit. The exact solution selected within this range can be calibrated to a mineralogical analysis of actual core samples, or based on knowledge of local geology.
The invention also recognizes and utilizes the fact that some logs are more accurate than others in resolving a given lithologic component. A particular log can be empirically weighted to a particular lithologic component. For instance, the neutron log may be far more accurate than the sonic lo g in resolving a coal streak; the gamma ray may be much more accurate than any other log in resolving shale. Prior art methods do not take this key factor into account.